Compressive sensing using the modified entropy functional Journal Article
| Authors: | Kose, K.; Gunay, O.; Cetin, A. E. |
| Article Title: | Compressive sensing using the modified entropy functional |
| Abstract: | In most compressive sensing problems, ℓ1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the ℓ1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregmanʼs row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. |
| Keywords: | set theory; entropy; compressed sensing; signal reconstruction; iterative methods; compressive sensing; modified entropy functional; iterative row-action methods; bregman-projection; projection onto convex sets; proximal splitting; bregman projection; entropy functional; row action |
| Journal Title: | Digital Signal Processing: A Review Journal |
| Volume: | 24 |
| ISSN: | 1051-2004 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2014-01-01 |
| Start Page: | 63 |
| End Page: | 70 |
| Language: | English |
| DOI: | 10.1016/j.dsp.2013.09.010 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Article -- Export Date: 3 January 2017 -- Source: Scopus |
